Identifying chemical substructures associated with adverse drug reactions

ABSTRACT

Embodiments of the present invention are directed to a computer-implemented method for generating a framework for analyzing adverse drug reactions. A non-limiting example of the computer-implemented method includes receiving to a processor, a plurality of drug chemical structures. The non-limiting example also includes receiving, to the processor, a plurality of known drug-adverse drug reaction associations. The non-limiting example also includes constructing, by the processor, a deep learning framework for each of a plurality of adverse drug reactions based at least in part upon the plurality of drug chemical structures and the plurality of known adverse-drug reaction associations.

BACKGROUND

The present invention generally relates to adverse drug reactions, andmore specifically, to identifying chemical substructures associated withadverse drug reactions.

Adverse drug reactions (ADRs) are unintended and harmful reactionscaused by normal uses of drugs. ADRs represent a significant publichealth problem all over the world. In the United States, it is estimatedthat over 2 million serious ADRs occur among hospitalized patients,resulting in over 100,000 deaths each year. Moreover, ADRs are acontributing factor to the high expenditure and low effectiveness oflaboratory-based pharmaceutical drug development. Readily availableinformation in the early stages of drug development can often be limitedto the chemical structure of a drug candidate. However, predicting andpreventing ADRs in the early stage of the drug development pipeline canhelp to enhance drug safety and reduce financial costs associated withdrug discovery.

Finding novel associations between chemical substructures and ADRs couldguide research efforts toward drug candidates that are more likely tolead to safe and efficacious drugs. For instance, if a chemicalsubstructure is determined to be associated with ADRs, researchers coulddesign drug candidates that do not incorporate such substructures.Elucidating such detailed relationships among chemical substructures andADRs could infer new knowledge for domain experts, for instance, thatcould be utilized to redesign a drug under development and, thus,increase investigative efforts leading to viable candidates.

SUMMARY

Embodiments of the present invention are directed to acomputer-implemented method for generating a framework for analyzingadverse drug reactions. A non-limiting example of thecomputer-implemented method includes receiving to a processor, aplurality of drug chemical structures. The non-limiting example alsoincludes receiving, to the processor, a plurality of known drug-adversedrug reaction associations. The non-limiting example also includesconstructing, by the processor, a deep learning framework for each of aplurality of adverse drug reactions based at least in part upon theplurality of drug chemical structures and the plurality of knownadverse-drug reaction associations. Such computer-implemented methodscan enable a-priori identification of chemical substructures likely toresult in ADRs.

Embodiments of the invention are directed to a computer program productfor analyzing adverse drug reactions. A non-limiting example of thecomputer program product includes a computer readable storage mediumhaving program instructions embodied therewith. The program instructionsare executable by a processor to cause the processor to perform amethod. A non-limiting example of the method includes receiving aplurality of drug chemical structures. The non-limiting example alsoincludes receiving a plurality of known drug-adverse drug reactionassociations. The non-limiting example also includes constructing a deeplearning framework for each of a plurality of adverse drug reactionsbased at least in part upon the plurality of drug chemical structuresand the plurality of known adverse-drug reaction associations. Suchcomputer program products can enable a-priori identification of chemicalsubstructures likely to result in ADRs.

Embodiments of the invention are directed to a processing system foranalyzing adverse drug reactions. A non-limiting example of theprocessing system includes a processor in communication with one or moretypes of memory. The processor can be configured to receive a pluralityof drug chemical structures. The processor can also be configured toreceive a plurality of known drug-adverse drug reaction associations.The processor can also be configured to construct a deep learningframework for each of a plurality of adverse drug reactions based atleast in part upon the plurality of drug chemical structures and theplurality of known adverse-drug reaction associations. Such processingsystems can enable a-priori identification of chemical substructureslikely to result in ADRs.

Embodiments of the present invention are directed to acomputer-implemented method for predicting chemical substructuresassociated with adverse drug reactions. A non-limiting example of thecomputer-implemented method includes generating a plurality of raw drugfeatures. The non-limiting example also includes pooling the pluralityof significant substructures into a plurality of fixed-sized vectors.The non-limiting example also includes generating a plurality offixed-length fingerprint representations based at least in part up onthe plurality of fixed-sized vectors. The non-limiting example alsoincludes building a final predictive model based at least in part uponthe fixed-length fingerprint representations. Such methods can generateaccurate chemical fingerprints associated with known ADRs.

Embodiments of the present invention are directed to a system forpredicting chemical substructures associated with adverse drugreactions. A non-limiting example of the system includes a drug-ADRassociation prediction module. A non-limiting example of the system alsoincludes a significant association identification module. A non-limitingexample of the system also includes a neighborhood substructureassociation module. A non-limiting example of the system also includes agrouping module. Such systems can generate accurate chemicalfingerprints associated with known ADRs.

Additional technical features and benefits are realized through thetechniques of the present invention. Embodiments and aspects of theinvention are described in detail herein and are considered a part ofthe claimed subject matter. For a better understanding, refer to thedetailed description and to the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The specifics of the exclusive rights described herein are particularlypointed out and distinctly claimed in the claims at the conclusion ofthe specification. The foregoing and other features and advantages ofthe embodiments of the invention are apparent from the followingdetailed description taken in conjunction with the accompanying drawingsin which:

FIG. 1 depicts a block diagram illustrating one example of a processingsystem for practice of the teachings herein;

FIG. 2 depicts a flow diagram of a method according to one or moreembodiments of the present invention;

FIG. 3 depicts a flow diagram of a method according to one or moreembodiments of the present invention;

FIG. 4 depicts an exemplary system according to one or more embodimentsof the present invention;

FIG. 5 illustrates an exemplary method according to one or moreembodiments of the present invention;

FIG. 6A illustrates an exemplary method according to one or moreembodiments of the present invention;

FIG. 6B illustrates an exemplary method according to one or moreembodiments of the present invention; and

FIG. 7 illustrates an exemplary method according to one or moreembodiments of the present invention.

The diagrams depicted herein are illustrative. There can be manyvariations to the diagram or the operations described therein withoutdeparting from the spirit of the invention. For instance, the actionscan be performed in a differing order or actions can be added, deletedor modified. Also, the term “coupled” and variations thereof describeshaving a communications path between two elements and does not imply adirect connection between the elements with no interveningelements/connections between them. All of these variations areconsidered a part of the specification.

In the accompanying figures and following detailed description of thedisclosed embodiments, the various elements illustrated in the figuresare provided with two or three digit reference numbers. With minorexceptions, the leftmost digit(s) of each reference number correspond tothe figure in which its element is first illustrated.

DETAILED DESCRIPTION

Various embodiments of the invention are described herein with referenceto the related drawings. Alternative embodiments of the invention can bedevised without departing from the scope of this invention. Variousconnections and positional relationships (e.g., over, below, adjacent,etc.) are set forth between elements in the following description and inthe drawings. These connections and/or positional relationships, unlessspecified otherwise, can be direct or indirect, and the presentinvention is not intended to be limiting in this respect. Accordingly, acoupling of entities can refer to either a direct or an indirectcoupling, and a positional relationship between entities can be a director indirect positional relationship. Moreover, the various tasks andprocess steps described herein can be incorporated into a morecomprehensive procedure or process having additional steps orfunctionality not described in detail herein.

The following definitions and abbreviations are to be used for theinterpretation of the claims and the specification. As used herein, theterms “comprises,” “comprising,” “includes,” “including,” “has,”“having,” “contains” or “containing,” or any other variation thereof,are intended to cover a non-exclusive inclusion. For example, acomposition, a mixture, process, method, article, or apparatus thatcomprises a list of elements is not necessarily limited to only thoseelements but can include other elements not expressly listed or inherentto such composition, mixture, process, method, article, or apparatus.

Additionally, the term “exemplary” is used herein to mean “serving as anexample, instance or illustration.” Any embodiment or design describedherein as “exemplary” is not necessarily to be construed as preferred oradvantageous over other embodiments or designs. The terms “at least one”and “one or more” are intended to include any integer number greaterthan or equal to one, i.e. one, two, three, four, etc. The terms “aplurality” are intended to include any integer number greater than orequal to two, i.e. two, three, four, five, etc. The term “connection” isintended to include both an indirect “connection” and a direct“connection.”

The terms “about,” “substantially,” “approximately,” and variationsthereof, are intended to include the degree of error associated withmeasurement of the particular quantity based upon the equipmentavailable at the time of filing the application. For example, “about”can include a range of ±8% or 5%, or 2% of a given value.

For the sake of brevity, conventional techniques related to making andusing aspects of the invention may or may not be described in detailherein. In particular, various aspects of computing systems and specificcomputer programs to implement the various technical features describedherein are well known. Accordingly, in the interest of brevity, manyconventional implementation details are only mentioned briefly herein orare omitted entirely without providing the well-known system and/orprocess details.

Referring to FIG. 1, there is shown an embodiment of a processing system100 for implementing the teachings herein. In this embodiment, thesystem 100 has one or more central processing units (processors) 101 a,101 b, 101 c, etc. (collectively or generically referred to asprocessor(s) 101). In one embodiment, each processor 101 can include areduced instruction set computer (RISC) microprocessor. Processors 101are coupled to system memory 114 and various other components via asystem bus 113. Read only memory (ROM) 102 is coupled to the system bus113 and can include a basic input/output system (BIOS), which controlscertain basic functions of system 100.

FIG. 1 further depicts an input/output (I/O) adapter 107 and a networkadapter 106 coupled to the system bus 113. I/O adapter 107 can be asmall computer system interface (SCSI) adapter that communicates with ahard disk 103 and/or tape storage drive 105 or any other similarcomponent. I/O adapter 107, hard disk 103, and tape storage device 105are collectively referred to herein as mass storage 104. Operatingsystem 120 for execution on the processing system 100 can be stored inmass storage 104. A network adapter 106 interconnects bus 113 with anoutside network 116 enabling data processing system 100 to communicatewith other such systems. A screen (e.g., a display monitor) 115 isconnected to system bus 113 by display adaptor 112, which can include agraphics adapter to improve the performance of graphics intensiveapplications and a video controller. In one embodiment, adapters 107,106, and 112 can be connected to one or more I/O busses that areconnected to system bus 113 via an intermediate bus bridge (not shown).Suitable I/O buses for connecting peripheral devices such as hard diskcontrollers, network adapters, and graphics adapters typically includecommon protocols, such as the Peripheral Component Interconnect (PCI).Additional input/output devices are shown as connected to system bus 113via user interface adapter 108 and display adapter 112. A keyboard 109,mouse 110, and speaker 111 all interconnected to bus 113 via userinterface adapter 108, which can include, for example, a Super I/O chipintegrating multiple device adapters into a single integrated circuit.

In exemplary embodiments of the invention, the processing system 100includes a graphics processing unit 130. Graphics processing unit 130 isa specialized electronic circuit designed to manipulate and alter memoryto accelerate the creation of images in a frame buffer intended foroutput to a display. In general, graphics processing unit 130 is veryefficient at manipulating computer graphics and image processing, andhas a highly parallel structure that makes it more effective thangeneral-purpose CPUs for algorithms where processing of large blocks ofdata is done in parallel.

Thus, as configured in FIG. 1, the system 100 includes processingcapability in the form of processors 101, storage capability includingsystem memory 114 and mass storage 104, input means such as keyboard 109and mouse 110, and output capability including speaker 111 and display115. In one embodiment, a portion of system memory 114 and mass storage104 collectively store an operating system such as the AIX® operatingsystem from IBM Corporation to coordinate the functions of the variouscomponents shown in FIG. 1.

Turning now to an overview of technologies that are more specificallyrelevant to aspects of the invention, according to the World HealthOrganization (WHO), an ADR is generally defined as an unintended andharmful reaction suspected to be caused by a drug taken under normalconditions. Identifying potential ADRs of drug candidates in the earlystage of the drug development pipeline can improve drug safety, reducerisks for patients, and reduce monetary costs to pharmaceuticalcompanies.

Information available in the early stages of drug development can belargely limited to the chemical structure of the drug candidate. Themolecular structures of drugs can be leveraged in drug development wherespecific chemical substructures of drugs responsible for the ADRs can beidentified. Thus, some existing studies on ADR prediction have focusedon analyzing the chemical properties of drug molecules. Elucidating suchdetailed relationships between such chemical substructures and ADRs haspotential to infer new knowledge for domain experts that can be used toredesign a drug under development and, thus, increase drug efficacywhile minimizing the risk to patients and monetary expenditureassociated with research and development. However mechanisms of ADRs canbe complicated and not well understood presenting several challenges.

In some embodiments of the invention, each drug molecule can berepresented in a suitable feature vector based upon its chemicalstructure and machine learning can be leveraged to predict ADRsa-priori. Some embodiments of the invention and have the capability ofexploring all possible chemical substructures available in a set ofdrugs or drug candidates.

Embodiments of the invention advantageously provide a neural fingerprintmethod in a simultaneous deep learning framework for ADR prediction suchthat label information (including drug-ADR associations) can be used inthe feature generation stage of a machine learning process. Someembodiments of the invention include interpretation and analysis ofgenerated features to evaluate their associations for the prediction ofADRs in new drugs.

Turning now to an overview of the aspects of the invention, one or moreembodiments of the invention address the above-described shortcomings ofthe prior art by providing a methodology and system for identifyingsubstructures of chemical compounds that have significant associationswith ADRs using a machine learning approach. Embodiments of theinvention can systematically identify sub structures of chemicalcompounds that have significant association with ADRs, which can provideactionable insights for drug design. “Significant association” as usedherein means an association that is statistically significant asdetermined by one or more statistical methods. Embodiments of thepresent invention can provide additional insights concerning theunderlying reasons that certain substructures can induce ADRs inaddition to predicting ADRs from drugs and drug candidates.

The above-described aspects of the invention address the shortcomings ofthe prior art by ranking substructure-ADR pairs obtains from generatedmodels to systematically analyze the relationships among groups ofchemical substructures within groups of related ADRs using bi-clusteringbased machine learning techniques. Through such techniques, drugdiscovery efforts can include the redesign of specific parts ofidentified substructures of a drug in response to and/or in connectionwith the relationship analysis.

Turning now to a more detailed description of aspects of the presentinvention, FIG. 2 depicts a method 200 of identifying chemicalsubstructures associated with ADRs according to one or more embodimentsof the present invention. As is shown, the method 200 includesconstructing a deep learning framework for each of a plurality of ADRsbased at least in part upon drug chemical structures and known drug-ADRassociations as shown at block 202. As used herein, “drug chemicalstructure” is understood to mean the complete chemical structure of apharmaceutical drug or candidate pharmaceutical drug. Known drug-ADRassociations include ADRs known to be associated with a drug throughclinical testing, therapeutic administration, and the like. The method200 also includes, as shown at block 204, analyzing the deep learningframework to determine substructures related to each ADR and generatesubstructure-ADR associations. As used herein, “substructure” isintended to mean a portion of a chemical structure of a chemicalcompound. The method 200 also includes, as shown at block 206,determining significant substructure-ADR associations and ranking thesignificant substructure-ADR associations. The method 200 also includes,as shown at block 208, grouping substructures and related ADRs usingbiclustering. Optionally, the method 200 includes outputting predicteddrug-ADR associations, significant chemical substructures, and/or globalsubstructure-ADR mapping as shown at block 210.

The deep learning framework can include a neural fingerprint basedpredictive model. In some embodiments of the invention, a convolutionalneural fingerprint framework is generated wherein the neighborhood ofeach atom is explored iteratively based upon hidden layers byrepresenting a drug in either a 2D or a 3D graph. ADR prediction can beformulated as a binary prediction problem, wherein a predictive model isbuilt for each ADR using chemical fingerprints as features.

In some embodiments of the invention, constructing a deep learningframework for each of a plurality of ADRs based at least in part upondrug chemical structures and known drug-ADR associations. Drug chemicalstructures and known drug-ADR associations can be obtained automaticallyor manually. Public databases contain a variety of information regardingknown drugs and ADRs, including chemical structural data and chemicaldata. These information sources can contain structured or unstructureddata. Drug chemical structures and known drug-ADR associations caninclude structured data, unstructured data, or both structured andunstructured data. As used herein, structured data includes data that iscategorized or grouped in accordance with a system of defined rules. Asused herein, unstructured data includes data that is not categorized orgrouped in accordance with a system of defined rules. For example,unstructured data includes, but is not limited to, data published injournal articles in a narrative format. In exemplary embodiments, knowndrug data includes data from databases generally known to persons ofordinary skill in the art. For example, known drug data can include datafrom the DrugBank database, UniProt, Unified Medical Language System TM,PubMed, and/or various scientific journals, including, but not limitedto, the Journal of Clinical Oncology, JAMA, BJC, and Clinical InfectiousDiseases. Adverse drug event data includes information related toadverse events associated with a drug. Adverse drug event data caninclude, for example, the incidence, prevalence, or severity of eventssuch as bleeding, paralysis, hyperkalemia.

FIG. 3 depicts an exemplary method 300 for constructing a deep learningframework for a plurality of adverse drug reactions according to one ormore embodiments of the present invention. The method 300 includes, asshown at block 302, generating raw drug features. The method 300 alsoincludes, as shown at block 304, generating convolutional feature maps.The method 300 also includes pooling substructures into fixed sizedvectors, as shown at block 306. The method 300 also includes, as shownat block 308, generating fixed-length fingerprint representations. Themethod 300 also includes, as shown at block 310, building a finalpredictive model.

Embodiments of the invention including generating raw drug features caninclude, for instance, representing each drug into a 2D or 3D graphicalstructure. After generation of the graphical structure, in someembodiments of the invention, chemical features for each constituentatom in the drug can be extracted. For example, known fingerprintalgorithms, such as ECFP, can be used to derive one or more featuressuch as atomic element, degree, numbers of attached hydrogen atoms,implicit valence, aromaticity indicator, and/or bond type. Each drug canbe represented by a matrix X∈R^(n) ^(x) ^(xd), where n_(x) representsthe number of atoms in drug X and d is the total number of features foreach atom. Let x_(i)∈R^(d) represent the feature vector of each atomi∈{1, . . . , n_(x)}.

Some embodiments of the invention include generating convolutionalfeature maps. A convolutional step can, for instance, represent asubstructure in a particular layer into a condensed feature vector. Ineach interaction, or layer, of the algorithm, each substructure incurrent layer l (represented by each atom i referred as center and anyneighbors explored in previous layers) can be expanded to include theimmediate neighbors of each atom belonging to that substructure.Subsequently, all atomic features and bonding information of the atomsincluded within the substructure from previous layers can beconcatenated into a large feature vector of size d_(l−1) and redefinedas new feature vector x_(i) ^(l−1)∈R^(d) ^(l−1) .

Each substructure can be obtained by starting the search from multipleatoms belonging to substructures and, thus, can be obtained frommultiple centers. To remove resultant redundancies, each substructurex_(i) ^(l−1) can be mapped into lower dimensions using a single layer ofneural network with d_(l−1) input nodes and d_(l) output nodes. A weightmatrix of H∈R^(d) ^(l) ^(xd) ^(l+1) can be defined as follows: x_(i)^(l)=f(x_(i) ^(l−1)H+b) where b∈R. In this instance, f is a smoothingfunction to reduce susceptibility to minor variations in substructure.

Some embodiments of the invention include pooling multiple substructuresinto fixed sized vectors. For example, after generation of convolutionalfeatures maps in each level, similar substructures can be pooled into afixed-sized feature vector of size K (hyper-parameter) using anotherlevel of neural network of weights F and a softmax, for instance, whichhas been shown to have a concise set of fingerprint representations forlarger drug molecules. A simple addition function can be used tosummarize the activation score of each atom that belongs to a givenmolecule in the pooling stage of the convolutional neural network.

In some embodiments of the invention, steps of generating convolutionalfeature maps and pooling multiples substructures into fixed-sizedvectors are iterated for each radius of the molecule up to a maximumradius of the substructure L using a separate hidden layer tosuccessively explore all possible substructures up to a maximum pathlength of 2L−1. Thereafter, in some embodiments of the invention,fingerprint vectors obtained from each layer are pooled (or summarized)into a final representation by summing them into a final fixed-lengthfingerprint representation.

Embodiments of the invention include building final predictive models.In some embodiments of the invention, upon generation of a finalfingerprint representation for each drug, a fully connected neuralnetwork can be used to evaluate the ability to predict an ADR. For eachADR, for example, drugs associated with the ADR can be labeled aspositives and the remaining drugs can be labeled as negatives. Apredictive model can be built, for instance, using L2-norm regularizedlogistic regression separately for each ADR using final fingerprintrepresentations as features. A loss function can be describe accordingto the following formula:

${\mathcal{L}\left( {Z,y,w} \right)} = {{\sum\limits_{i}\;\left( {y_{i} - {f\left( {{z_{i}*w} + b} \right)}} \right)} + {\lambda{w}_{2}^{2}}}$

wherein, Z is the matrix containing all fingerprints for each drugdenoted as z_(i)∈R^(W), is a maximum radius for substructures, K is thenumber of fingerprints with the best F−1 score, λ is a regularizationparameter, and f is a non-linear function (for example rectificationwith rectified linear unit (ReLU)). Batch normalization can be used tooptimize each batch of size 100 using a known algorithm, such as ADAM.Hyper-parameters of the algorithms such as λ, R, K, can be selectedduring cross-validations. Further tuning of parameters can be performedfor neural fingerprints based upon F1-scores, such as the numbers ofneurons in the hidden layers and in the final layer.

Embodiments of the invention can include generating substructure-ADRassociations. Extraction and interpretation of the importantfingerprints of drugs can provide useful information concerning ADRs.For example, known ADRs can be analyzed with the deep learning frameworkto by determine substructures related to each ADR by identifying the toppredictive fingerprints based upon learned weights from the final layerof the neural network. For each of the top predictive fingerprints, eachlayer can be investigated to identify atoms and associated drugmolecules having the highest activation for the fingerprint during afirst convolution step. Subsequently, substructures can be reconstructedby using the identified atom as center and expanding the neighborhood upto the associated layer.

FIG. 4 depicts an exemplary system 400 for identifying chemicalsubstructures associated with ADRs according to one or more embodimentsof the present invention. The system 400 includes an input includingtraining data 402, a deep learning ADR prediction hub 404, and an output406. Training data can include, for instance, chemical structures ofdrugs as represented by neighborhood-based fingerprints and alreadyknown drug-ADR associations. In some embodiments, not illustrated inFIG. 4, the system can include optional input, such as domain knowledgeabout potential substructures of ADRs or existing relationships amongsubstructures of drugs. The ADR prediction hub 404 can include adrug-ADR association prediction module 408, a significant associationidentification module 410, a neighborhood substructure associationmodule 412, and/or a grouping module 414. The output 406 can include,for example, a predicted drug-ADR association 416. The output 406 canalso include significant chemical substructures for ADRs 418.“Significant chemical substructures” as used herein means chemicalsubstructures that have a statistically significant association with anADR. The output 406 can also include global substructure-ADR maps 420.

In some embodiments of the invention, drug-ADR association predictionmodule 408 can construct a deep learning framework for each of aplurality of ADRs based at least in part upon drug chemical structuresand known drug-ADR associations.

Significant association identification module 410 can analyze the deeplearning frameworks to determine substructures related to each ADR andgenerate substructure-ADR associations.

Neighborhood substructure association module 412 can determinesignificant substructure-adverse drug reaction associations, for examplewith a chi-squared test, and rank significant substructure-adverse drugreaction associations, for example, according to statisticalsignificance.

Grouping module 414 can group substructures and related ADRs usingbiclustering.

FIG. 5 illustrates generation of substructure-ADR associations accordingto exemplary embodiments of the invention. For example, a combinedlearning framework using deep learning networks can be employed. As isshown at 500, molecular structures of drugs can be analyzed to generatefingerprint feature representations. Subsequently, as shown at 502, afully connected neural network as a predictive model can be applied tothe fingerprint feature representations. A plurality of ADRs 508, 510,512 can be output, as is illustrated at 504, and associated at 506 withchemical substructures 514, 516, and 518, respectively.

Embodiments of the invention include determining significantsubstructure-adverse drug reaction associations. For example, FIG. 6Aillustrates a confusion matrix calculated for a given substructure Aregarding exemplary ADR X from a SIDER database. As is illustrated, arepresents the number of drugs that contain substructure A and cause ADRX; b is the number of drugs that do not contain substructure A buttrigger ADR X; c is the number of drugs that contain substructure A buthave no association towards ADR X; and d is the number of drugs that donot contain substructure A and have no association towards ADR X. Someembodiments of the invention include calculating a p value usingchi-squared tests and odds ratio to evaluate the association strengthbetween substructure A and ADR X. In some embodiments of the invention,substructure-ADR associations are ranked based upon a statisticalanalysis.

Embodiments of the invention include grouping substructures and relatedADRs using biclustering. For instance, substructures that are associatedwith the ADRs can be further grouped into higher levels because many ofthe ADRs are inherently related. For example, available ADRs can beclassified into a hierarchical graph by organizing them from specific togeneric categories.

For instance, to identify substructures that are responsible for aparticular group of ADRs could provide an early guideline for avoidingthose related substructures or their continuous spectrum ofrepresentations during drug development. Accordingly, significantsubstructure-ADR pairs can be grouped based upon a guilt by associationprinciple. For example, significant substructure-ADR associations can beincluded in a bipartite graph, where substructures are represented inone layer and ADRs in another layer and, wherein an edge between themrepresents a significant association obtained from the previous step.Consequently, biclustering algorithms can be applied to find the higherlevel groupings (bi-cliques) of sub structure-ADR pairs.

FIG. 6B illustrates global mapping of substructures and ADRs.Statistically significant substructures can be mapped, for examplegraphically, to associated ADRs.

FIG. 7 illustrates an exemplary combined learning framework using a deeplearning network according to one or more embodiments of the invention.A plurality of drug representations including atoms and atomicproperties can be obtained. These can be used to build convolutionalnetworks to map fingerprints, as is illustrated. For example, buildingconvolutional networks can be analogous or equivalent to hashing. Hiddenlayers of convolutional network can be included for each iteration (orradius). The exemplary framework can include pooling to generate finalfingerprints. The final fingerprints can be used to generate a fullyconnected network for predicting an ADR.

Embodiments of the invention can automatically identify substructures ofchemical compounds that have significant associations with ADRs using adeep learning approach, which can provide actionable insights for drugdevelopment and safety. Embodiments of the invention can ranksubstructure-ADR pairs obtained from deep learning models tosystematically analyze the relationships among the groups of chemicalsubstructures with groups of related ADRs using biclustering basedmachine learning techniques. Embodiments of the invention are useful fordiscovering relationships among chemical features and ADRs and can alsobe used for a small set of ADRs. Some embodiments of the invention candefine chemical substructures without a-priori substructure definition.Embodiments of the invention can advantageously explore the statisticalsignificance of chemical substructure-ADR pairs and their higher ordergroupings.

Embodiments of the invention can leverage a state-of the artconvolutional deep learning network to simultaneously construct chemicalfingerprints and their capabilities toward predicting ADR in a singlelearning framework. Such learning steps can result in a parsimonious setof fingerprints, for example, because the model can be limited to learnonly those fingerprints that are predictive of ADRs, thus automaticallyfiltering irrelevant fingerprints without post-processing.

Embodiments of the invention can be useful for predicting adverse drugreactions based upon chemical structure. Embodiments of the inventioncan also be used for other tasks in drug design, such as prediction ofdrug-drug interactions.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instruction by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdescribed herein.

What is claimed is:
 1. A computer program analyzing adverse drugreactions, the computer program product comprising: a computer readablestorage medium readable by a processing circuit and storing programinstructions for execution by the processing circuit for performing amethod comprising: receiving a plurality of drug chemical structures;receiving a plurality of known drug-adverse drug reaction associations;constructing a deep learning framework for each of a plurality ofadverse drug reactions based at least in part upon the plurality of drugchemical structures and the plurality of known drug-adverse drugreaction associations, wherein constructing the deep learning frameworkcomprises defining a plurality of neighborhood-based fingerprints foreach of the plurality of drug chemical structures using a plurality ofhidden layers and generating a convolutional feature map comprising aplurality of convolutional steps, wherein each convolutional stepencodes a respective neighborhood-based fingerprint at a respectivehidden layer, wherein each fingerprint is obtained by starting frommultiple atoms belonging to said fingerprint and resultant redundanciesare removed by mapping each fingerprint into a lower dimension using asingle layer of the deep learning framework; analyzing the deep learningframeworks to determine a set of substructure-adverse drug reactionassociations; identifying a plurality of top predictive fingerprints forthe set of sub structure-adverse drug reaction associations based uponlearned weights from a final layer of the deep learning framework; foreach of the plurality of top predictive fingerprints, investigating eachlayer of the deep learning framework to identify atoms having a highestactivation for the respective fingerprint; reconstructing a set ofsubstructures by starting from each identified atom and expanding theneighborhood up to the respective layer, wherein reconstructing from afirst identified atom on a first layer of the deep learning frameworkresults in expanding the respective neighborhood up to the first layer,and wherein reconstructing from a second identified atom on a secondlayer of the deep learning framework results in expanding the respectiveneighborhood up to the second layer; calculating, for each substructure,a p value using a chi-squared test to evaluate a relative associationstrength between the substructure and the respective adverse drugreaction association; ranking the substructure-adverse drug reactionassociations according to statistical significance using the p values;and redesigning a candidate substructure of a candidate drug to avoid adetermined substructure-adverse drug reaction association.
 2. Thecomputer program product according to claim 1, wherein the methodfurther comprises grouping substructures and related adverse drugreactions with biclustering.
 3. The computer program product accordingto claim 2, wherein the method further comprises outputting a chemicalsubstructure-adverse drug reaction association.
 4. The computer programproduct according to claim 2, wherein the method further comprisesoutputting a substructure-adverse drug reaction map.
 5. A processingsystem for analyzing adverse drug reactions, comprising: a processor incommunication with one or more types of memory, the processor configuredto: receive a plurality of drug chemical structures; receive a pluralityof known drug-adverse drug reaction associations; construct a deeplearning framework for each of a plurality of adverse drug reactionsbased at least in part upon the plurality of drug chemical structuresand the plurality of known drug-adverse drug reaction associations,wherein constructing the deep learning framework comprises defining aplurality of neighborhood-based fingerprints for each of the pluralityof drug chemical structures using a plurality of hidden layers andgenerating a convolutional feature map comprising a plurality ofconvolutional steps, wherein each convolutional step encodes arespective neighborhood-based fingerprint at a respective hidden layer,wherein each fingerprint is obtained by starting from multiple atomsbelonging to said fingerprint and resultant redundancies are removed bymapping each fingerprint into a lower dimension using a single layer ofthe deep learning framework; analyze the deep learning frameworks todetermine a set of substructure-adverse drug reaction associations;identify one or more top predictive fingerprints for the set ofsubstructure-adverse drug reaction associations based upon learnedweights from a final layer of the deep learning framework; for each ofthe one or more top predictive fingerprints, investigate each layer ofthe deep learning framework to identify atoms having a highestactivation for the respective fingerprint; reconstruct a set ofsubstructures by starting from each identified atom and expanding theneighborhood up to the respective layer, wherein reconstructing from afirst identified atom on a first layer of the deep learning frameworkresults in expanding the respective neighborhood up to the first layer,and wherein reconstructing from a second identified atom on a secondlayer of the deep learning framework results in expanding the respectiveneighborhood up to the second layer; calculate, for each substructure, ap value using a chi-squared test to evaluate a relative associationstrength between the substructure and the respective adverse drugreaction association; rank the substructure-adverse drug reactionassociations according to statistical significance using the p values;and redesigning a candidate substructure of a candidate drug to avoid adetermined substructure-adverse drug reaction association.
 6. Theprocessing system according to claim 5, wherein the processor isconfigured to group substructures and related adverse drug reactionswith biclustering.
 7. The processing system according to claim 5,wherein the processor is configured to output a significant chemicalsubstructure.
 8. The processing system according to claim 5, wherein theprocessor is configured to output a substructure-adverse drug reactionmap.